Biology and Clinical Implications of the 19q13 Aggressive Prostate Cancer Susceptibility Locus.

Genome-wide association studies (GWAS) have identified rs11672691 at 19q13 associated with aggressive prostate cancer (PCa). Here, we independently confirmed the finding in a cohort of 2,738 PCa patients and discovered the biological mechanism underlying this association. We found an association of the aggressive PCa-associated allele G of rs11672691 with elevated transcript levels of two biologically plausible candidate genes, PCAT19 and CEACAM21, implicated in PCa cell growth and tumor progression. Mechanistically, rs11672691 resides in an enhancer element and alters the binding site of HOXA2, a novel oncogenic transcription factor with prognostic potential in PCa. Remarkably, CRISPR/Cas9-mediated single-nucleotide editing showed the direct effect of rs11672691 on PCAT19 and CEACAM21 expression and PCa cellular aggressive phenotype. Clinical data demonstrated synergistic effects of rs11672691 genotype and PCAT19/CEACAM21 gene expression on PCa prognosis. These results provide a plausible mechanism for rs11672691 associated with aggressive PCa and thus lay the ground work for translating this finding to the clinic.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression.

Prostate cancer is among the most common cancers in men, with a large fraction of the individual risk attributable to heritable factors. A majority of the diagnosed cases does not lead to a lethal disease, and hence biological markers that can distinguish between indolent and fatal forms of the disease are of great importance for guiding treatment decisions. Although over 300 genetic variants are known to be associated with prostate cancer risk, few have been associated with the risk of an aggressive disease. One such variant is rs77559646 located in ANO7. This variant has a dual function. It constitutes a missense mutation in the short isoform of ANO7 and a splice region mutation in full-length ANO7. In this study, we have analyzed the impact of the variant allele of rs77559646 on ANO7 mRNA splicing using a minigene splicing assay and by performing splicing analysis with the tools IRFinder (intron retention finder), rMATS (replicate multivariate analysis of transcript splicing) and LeafCutter on RNA sequencing data from prostate tissue of six rs77559646 variant allele carriers and 43 non-carriers. The results revealed a severe disruption of ANO7 mRNA splicing in rs77559646 variant allele carriers. Immunohistochemical analysis of prostate samples from patients homozygous for the rs77559646 variant allele demonstrated a loss of apically localized ANO7 protein. Our study is the first to provide a mechanistic explanation for the impact of a prostate cancer risk SNP on ANO7 protein production. Furthermore, the rs77559646 variant is the first known germline loss-of-function mutation described for ANO7. We suggest that loss of ANO7 contributes to prostate cancer progression.

Transcripts of the Prostate Cancer-Associated Gene ANO7 Are Retained in the Nuclei of Prostatic Epithelial Cells.

Prostate cancer affects millions of men globally. The prostate cancer-associated gene ANO7 is downregulated in advanced prostate cancer, whereas benign tissue and low-grade cancer display varying expression levels. In this study, we assess the spatial correlation between ANO7 mRNA and protein using fluorescent in situ hybridization and immunohistochemistry for the detection of mRNA and protein in parallel sections of tissue microarrays prepared from radical prostatectomy samples. We show that ANO7 mRNA and protein expression correlate in prostate tissue. Furthermore, we show that ANO7 mRNA is enriched in the nuclei of the luminal cells at 89% in benign ducts and low-grade cancer, and at 78% in high-grade cancer. The nuclear enrichment of ANO7 mRNA was validated in prostate cancer cell lines 22Rv1 and MDA PCa 2b using droplet digital polymerase chain reaction (ddPCR) on RNA isolated from nuclear and cytoplasmic fractions of the cells. The nuclear enrichment of ANO7 mRNA was compared to the nuclearly-enriched lncRNA MALAT1, confirming the surprisingly high nuclear retention of ANO7 mRNA. ANO7 has been suggested to be used as a diagnostic marker and a target for immunotherapy, but a full comprehension of its role in prostate cancer progression is currently lacking. Our results contribute to a better understanding of the dynamics of ANO7 expression in prostatic tissue.

Clinical features and spectrum of NOTCH3 variants in Finnish patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).

OBJECTIVES: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a cerebral small vessel disease caused by pathogenic variants in the NOTCH3 gene. In Finland, the majority of CADASIL patients carry the pathogenic founder variant c.397C>T, (p.Arg133Cys), but the spectrum of other NOTCH3 variants has not been investigated previously. The aim of the study was to investigate the spectrum and prevalence of NOTCH3 variants Finnish CADASIL patients and to examine the clinical features associated with them. MATERIALS AND METHODS: The spectrum of NOTCH3 variants and the clinical features associated with them were retrospectively examined in 294 Finnish CADASIL patients tested during January 1996 to October 2021 in the Medical Genetics laboratory of Department of Genomics of Turku University Hospital, where practically all samples of patients with suspected CADASIL in Finland are investigated. RESULTS: The most common NOTCH3 variants in the study cohort were c.397C>T, (p.Arg133Cys) (68%) and c.3206A>G p.(Tyr1069Cys) (18%), but other less common NOTCH3 variants were detected in as many as 14% of the patients. Eight of the detected NOTCH3 variants were novel: c.520T>A,p.(Cys174Ser), c.836A>G,p.(Gln279Arg), c.1369T>G,p.(Cys457Gly), c.1338C>G,p.(Cys446Trp), c.1564T>G,p.(Cys522Gly), c.2848T>G,p.(Cys950Gly), c.6102dup,p.(Gly2035Argfs*60), and c.2410+6C>G. Other NOTCH3 variants than p.Arg133Cys and p.Tyr1069Cys were more often associated with more severe clinical features. CONCLUSION: This study revealed the genetic and clinical spectrum of CADASIL in the Finnish population. Sequencing of the whole NOTCH3 gene performing a gene-panel or exome sequencing is recommended when suspecting CADASIL.

Genetic analysis reveals novel variants for vascular cognitive impairment.

OBJECTIVES: The genetic background of vascular cognitive impairment (VCI) is poorly understood compared to other dementia disorders. The aim of the study was to investigate the genetic background of VCI in a well-characterized Finnish cohort. MATERIALS & METHODS: Whole-exome sequencing (WES) was applied in 45 Finnish VCI patients. Copy-number variant (CNV) analysis using a SNP array was performed in 80 VCI patients. This study also examined the prevalence of variants at the miR-29 binding site of COL4A1 in 73 Finnish VCI patients. RESULTS: In 40% (18/45) of the cases, WES detected possibly causative variants in genes associated with cerebral small vessel disease (CSVD) or other neurological or stroke-related disorders. These variants included HTRA1:c.847G>A p.(Gly283Arg), TREX1:c.1079A>G, p.(Tyr360Cys), COLGALT1:c.1411C>T, p.(Arg471Trp), PRNP: c.713C>T, p.(Pro238Leu), and MTHFR:c.1061G>C, p.(Gly354Ala). Additionally, screening of variants in the 3'UTR of COL4A1 gene in a sub-cohort of 73 VCI patients identified a novel variant c.*36T>A. CNV analysis showed that pathogenic CNVs are uncommon in VCI. CONCLUSIONS: These data support pathogenic roles of variants in HTRA1, TREX1 and in the 3'UTR of COL4A1 in CSVD and VCI, and suggest that vascular pathogenic mechanisms are linked to neurodegeneration, expanding the understanding of the genetic background of VCI.

Recurrent moderate-risk mutations in Finnish breast and ovarian cancer patients.

Mutations in BRCA1 and BRCA2 genes predispose to breast and ovarian cancer (BC/OC) with a high lifetime risk, whereas mutations in PALB2, CHEK2, ATM, FANCM, RAD51C and RAD51D genes cause a moderately elevated risk. In the Finnish population, recurrent mutations have been identified in all of these genes, the latest being CHEK2 c.319+2T>A and c.444+1G>A. By genotyping 3,156 cases and 2,089 controls, we estimated the frequencies of CHEK2 c.319+2T>A and c.444+1G>A in Finnish BC patients. CHEK2 c.319+2T>A was detected in 0.7% of the patients, and it was associated with a high risk of BC in the unselected patient group (OR = 5.40 [95% CI 1.58-18.45], p = 0.007) and similarly in the familial patient group. CHEK2 c.444+1G>A was identified in 0.1% of all patients. Additionally, we evaluated the combined prevalence of recurrent moderate-risk gene mutations in 2,487 BC patients, 556 OC patients and 261 BRCA1/2 carriers from 109 families. The overall frequency of the mutations was 13.3% in 1,141 BRCA1/2-negative familial BC patients, 7.5% in 1,727 unselected BC patients and 7.2% in 556 unselected OC patients. At least one moderate-risk gene mutation was found in 12.5% of BRCA1 families and 7.1% of BRCA1 index patients, as well as in 17.0% of BRCA2 families and 11.3% of BRCA2 index patients, and the mutations were associated with an additional risk in the BRCA1/2 index patients (OR = 2.63 [1.15-5.48], p = 0.011). These results support gene panel testing of even multiple members of BC families where several mutations may segregate in different individuals.

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.

Risk Prediction of Prostate Cancer with Single Nucleotide Polymorphisms and Prostate Specific Antigen.

PURPOSE: Combined information on single nucleotide polymorphisms and prostate specific antigen offers opportunities to improve the performance of screening by risk stratification. We aimed to predict the risk of prostate cancer based on prostate specific antigen together with single nucleotide polymorphism information. MATERIALS AND METHODS: We performed a prospective study of 20,575 men with prostate specific antigen testing and 4,967 with a polygenic risk score for prostate cancer based on 66 single nucleotide polymorphisms from the Finnish population based screening trial of prostate cancer and 5,269 samples of 7 single nucleotide polymorphisms from the Finnish prostate cancer DNA study. A Bayesian predictive model was built to estimate the risk of prostate cancer by sequentially combining genetic information with prostate specific antigen compared with prostate specific antigen alone in study subjects limited to those with prostate specific antigen 4 ng/ml or above. RESULTS: The posterior odds of prostate cancer based on 7 single nucleotide polymorphisms together with the prostate specific antigen level ranged from 3.7 at 4 ng/ml, 14.2 at 6 and 40.7 at 8 to 98.2 at 10 ng/ml. The ROC AUC was elevated to 88.8% (95% CI 88.6-89.1) for prostate specific antigen combined with the risk score based on 7 single nucleotide polymorphisms compared with 70.1% (95% CI 69.6-70.7) for prostate specific antigen alone. It was further escalated to 96.7% (95% CI 96.5-96.9) when all prostate cancer susceptibility polygenes were combined. CONCLUSIONS: Expedient use of multiple genetic variants together with information on prostate specific antigen levels better predicts the risk of prostate cancer than prostate specific antigen alone and allows for higher prostate specific antigen cutoffs. Combined information also provides a basis for risk stratification which can be used to optimize the performance of prostate cancer screening.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

ANO7 is associated with aggressive prostate cancer.

Prostate cancer is one of the most common and heritable human cancers. Our aim was to find germline biomarkers that can predict disease outcome. We previously detected predisposing signals at 2q37, the location of the prostate specific ANO7 gene. To investigate, in detail, the associations between the ANO7 gene and PrCa risk and disease aggressiveness, ANO7 was sequenced in castration resistant tumors together with samples from unselected PrCa patients and unaffected males. Two pathogenic variants were discovered and genotyped in 1769 patients and 1711 unaffected males. Expression of ANO7 vs. PrCa aggressiveness was investigated. Different databases along with Swedish and Norwegian cohorts were used for validation. Case-control and aggressive vs. nonaggressive association analyses were performed against risk and/or cancer aggressiveness. The ANO7 mRNA level and patient survival were analyzed using expression data from databases. Variant rs77559646 showed both risk (OR 1.40; p = 0.009, 95% CI 1.09-1.78) and association with aggressive PrCa (Genotype test p = 0.04). It was found to be an eQTL for ANO7 (Linear model p-values for Finnish patients p = 0.009; Camcap prostate tumor p = 2.53E-06; Stockholm prostate tumor cohort p = 1.53E-13). rs148609049 was not associated with risk, but was related to shorter survival (HR 1.56; 95% CI 1.03-2.36). High ANO7 expression was independently linked to poor survival (HR 18.4; 95% CI 1.43-237). ANO7 genotypes correlate with expression and biochemical relapse, suggesting that ANO7 is a potential PrCa susceptibility gene and that its elevated expression correlates with disease severity and outcome.

Prostate cancer risk regions at 8q24 and 17q24 are differentially associated with somatic TMPRSS2:ERG fusion status.

Molecular and epidemiological differences have been described between TMPRSS2:ERG fusion-positive and fusion-negative prostate cancer (PrCa). Assuming two molecularly distinct subtypes, we have examined 27 common PrCa risk variants, previously identified in genome-wide association studies, for subtype specific associations in a total of 1221 TMPRSS2:ERG phenotyped PrCa cases. In meta-analyses of a discovery set of 552 cases with TMPRSS2:ERG data and 7650 unaffected men from five centers we have found support for the hypothesis that several common risk variants are associated with one particular subtype rather than with PrCa in general. Risk variants were analyzed in case-case comparisons (296 TMPRSS2:ERG fusion-positive versus 256 fusion-negative cases) and an independent set of 669 cases with TMPRSS2:ERG data was established to replicate the top five candidates. Significant differences (P < 0.00185) between the two subtypes were observed for rs16901979 (8q24) and rs1859962 (17q24), which were enriched in TMPRSS2:ERG fusion-negative (OR = 0.53, P = 0.0007) and TMPRSS2:ERG fusion-positive PrCa (OR = 1.30, P = 0.0016), respectively. Expression quantitative trait locus analysis was performed to investigate mechanistic links between risk variants, fusion status and target gene mRNA levels. For rs1859962 at 17q24, genotype dependent expression was observed for the candidate target gene SOX9 in TMPRSS2:ERG fusion-positive PrCa, which was not evident in TMPRSS2:ERG negative tumors. The present study established evidence for the first two common PrCa risk variants differentially associated with TMPRSS2:ERG fusion status. TMPRSS2:ERG phenotyping of larger studies is required to determine comprehensive sets of variants with subtype-specific roles in PrCa.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

This corrects the article DOI: 10.1038/bjc.2016.50.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Motivation: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . Contact: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies.

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Alcohol consumption and prostate cancer incidence and progression: A Mendelian randomisation study.

Prostate cancer is the most common cancer in men in developed countries, and is a target for risk reduction strategies. The effects of alcohol consumption on prostate cancer incidence and survival remain unclear, potentially due to methodological limitations of observational studies. In this study, we investigated the associations of genetic variants in alcohol-metabolising genes with prostate cancer incidence and survival. We analysed data from 23,868 men with prostate cancer and 23,051 controls from 25 studies within the international PRACTICAL Consortium. Study-specific associations of 68 single nucleotide polymorphisms (SNPs) in 8 alcohol-metabolising genes (Alcohol Dehydrogenases (ADHs) and Aldehyde Dehydrogenases (ALDHs)) with prostate cancer diagnosis and prostate cancer-specific mortality, by grade, were assessed using logistic and Cox regression models, respectively. The data across the 25 studies were meta-analysed using fixed-effect and random-effects models. We found little evidence that variants in alcohol metabolising genes were associated with prostate cancer diagnosis. Four variants in two genes exceeded the multiple testing threshold for associations with prostate cancer mortality in fixed-effect meta-analyses. SNPs within ALDH1A2 associated with prostate cancer mortality were rs1441817 (fixed effects hazard ratio, HRfixed = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HRfixed = 1.43; 95%CI:1.14,1.79; p values = 0.002) was associated with prostate cancer mortality in men with low-grade prostate cancer. These results suggest that alcohol consumption is unlikely to affect prostate cancer incidence, but it may influence disease progression.

Investigating the possible causal role of coffee consumption with prostate cancer risk and progression using Mendelian randomization analysis.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all-cause and prostate cancer-specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high-grade compared to low-grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all-cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer-specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

A genetic variant near GATA3 implicated in inherited susceptibility and etiology of benign prostatic hyperplasia (BPH) and lower urinary tract symptoms (LUTS).

BACKGROUND: Benign prostatic hyperplasia (BPH) and associated lower urinary tract symptoms (LUTS) are common conditions. Little is known about their etiologies except that studies have suggested a substantial heritable component. Our objective is to provide a comprehensive, genome-wide evaluation of inherited risks and possible mechanisms of etiology in BPH. METHODS: We performed a three-stage, genome-wide association study (GWAS) of men from three independent populations, the REduction by DUtasteride of prostate Cancer Events (REDUCE) trial, the CLUE II cohort, and a Finnish hospital-based population. DNA samples were genotyped using the Illumina HumanOmniExpress BeadChip in REDUCE and CLUE II, and using the Sequenom iPLEX system for the confirmation stage in the Finnish population. A logistic regression model was used to evaluate the association between each SNP and BPH/LUTS. RESULTS: Fourteen SNPs reached P < 5.0 × 10-4 in the meta-analysis of the two GWASs (CLUE II and REDUCE). A total of 773 SNPs were chosen for the confirmation step in the Finish cohort. Only one SNP (rs17144046) located ∼489 kb downstream of GATA3 remained significant after correction for multiple testing (P < 6.5 × 10-5 ). This SNP marginally reached the GWAS significance level after performing a meta-analysis of the three stages (P-meta = 8.89 × 10-7 ). Expression quantitative trait loci (eQTL) analyses showed that the risk allele (G) of rs17144046 was significantly associated with increased expression of GATA3 (P = 0.017). Reported studies indicated a close correlation between GATA3 and BPH pathogenesis and progression. CONCLUSIONS: Rs17144046 located near GATA3 was significantly associated with BPH/LUTS in three independent populations, but did not reach a stringent GWAS significance level. Genetic variants of GATA3 may play a role in the inherited susceptibility and etiology of BPH/LUTS. Further research in this area is needed.